Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Bayesian analysis of a change-point in exponential families with applications
Computational Statistics & Data Analysis
Machine Learning - Special issue on context sensitivity and concept drift
SEDA: an architecture for well-conditioned, scalable internet services
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Introduction: Service-oriented computing
Communications of the ACM - Service-oriented computing
Induction in Time-Varying Domains: Motivation, Origins, and Encouragements
ECBS '04 Proceedings of the 11th IEEE International Conference and Workshop on Engineering of Computer-Based Systems
Exact and efficient Bayesian inference for multiple changepoint problems
Statistics and Computing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Recursive Bayesian location of a discontinuity in time series
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A dual-layered model for web services representation and composition
Journal of Intelligent Information Systems
ACM Computing Surveys (CSUR)
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Knowledge and Information Systems
Statistical outlier detection using direct density ratio estimation
Knowledge and Information Systems
Context change detection for resource allocation in service-oriented systems
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Complex Services Availability in Service Oriented Systems
ICSENG '11 Proceedings of the 2011 21st International Conference on Systems Engineering
Anomaly detection in IP networks
IEEE Transactions on Signal Processing
An online kernel change detection algorithm
IEEE Transactions on Signal Processing - Part II
Key factors in web latency savings in an experimental prefetching system
Journal of Intelligent Information Systems
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In real-life situations characteristics of Web service systems evolve in time. Therefore, change detection techniques become substantial elements of adaptive procedures for Web service systems management, such as resource allocation and anomaly detection methods. In this paper, we propose an on-line change detector which uses the Bayesian inference. We define two models which describe situations with one change and no change within data. Next we apply Bayesian model comparison for change detection. In order to obtain analytical expressions of model evidences used in the model comparison we provide a coherent framework of change detection which focuses on an approximation of the Bayes factor. The proposed solution, contrary to state-of-the-art methods, works in an on-line fashion and the algorithm's computational complexity is proportional to the constant size of the shifting window. Low computational complexity of the change detector enables its application in complex computer networks. At the end of the research paper, the quality of the proposed algorithm is examined using simulated Web service system.